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Cerca | Lliura | Ajuda | Servei de Biblioteques | Sobre el DDD | Català English Español | |||||||||
| Pàgina inicial > Articles > Articles publicats > Comparative analysis of tools to predict rapid progression in autosomal dominant polycystic kidney disease |
| Data: | 2022 |
| Resum: | Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disease and shows a wide phenotype. Only patients with rapid progression (RP) are included in clinical trials or are approved to receive disease-modifying drugs. This study aims at comparing different available predictive tools in ADPKD with the Mayo classification (MC) identification of rapid progressors based on high total kidney volume (TKV) according to age. Methods: A total of 164 ADPKD patients were recruited retrospectively from a single centre. The performance of diverse tools to identify RP defined as being in MC categories 1C-1E was assessed. Results: A total of 118 patients were MC 1C-1E. The algorithm developed by the European Renal Association-European Dialysis and Transplant Association Working Group on Inherited Kidney Disorders/European Renal Best Practice had a low sensitivity in identifying MC 1C-1E. The sensitivity and specificity of TKV to predict RP depend on the cut-off used. A kidney length of. |
| Ajuts: | Ministerio de Economía y Competitividad RD16/0009/0019 Instituto de Salud Carlos III PI18/00362 Instituto de Salud Carlos III PI19/01633 Instituto de Salud Carlos III PT20/00196 |
| Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. |
| Llengua: | Anglès |
| Document: | Article ; recerca ; Versió publicada |
| Matèria: | ADPKD ; Mayo classification ; Prediction ; PROPKD ; Rapid progression ; Total kidney volume |
| Publicat a: | Clinical Kidney Journal, Vol. 15 Núm. 5 (january 2022) , p. 912-921, ISSN 2048-8513 |
10 p, 1.6 MB |